Course catalogue doctoral education - VT21

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Title Causal Inference from observational data: emulating a target trial
Course number 2960
Programme Epidemiology
Language English
Credits 1.5
Date 2017-03-27 -- 2017-03-29
Responsible KI department The institute of Environmental Medicine
Specific entry requirements Courses ""Epidemiology I: Introduction to epidemiology"", ""Epidemiology II: Design of epidemiological studies"", ""Biostatistics I: Introduction for epidemiologists"", ""Biostatistics II: Logistic regression for epidemiologists"", and either ""Causal inference for epidemiological research"" (course 2416) or ""Causal Inference from observational data"" (course 2462) or corresponding courses.
Purpose of the course This advanced course on causal inference emphasizes graphs and conceptualization in simple settings but also introduces statistical methods for time-varying exposures.
Intended learning outcomes At the end of the course the student should be able to:
- formulate sufficiently well-defined comparative effectiveness questions,
- specify the protocol of the (hypothetical) target trial,
- design analyses of observational data that emulate the protocol of the target trial,
- identify key assumptions for a correct emulation of the target trial,
- decide when g-methods are required for data analysis, and
- critique observational studies for comparative effectiveness research.
Contents of the course Causal inference from observational data is a key task of epidemiology and of allied sciences such as sociology, education, behavioral sciences, demography, economics, health services research, etc. These disciplines share a methodological framework for causal inference that has been developed over the last decades.

This course presents this unifying causal theory and shows how epidemiologic concepts and methods can be understood within this general framework. Specifically, this course strives to a) formally define causal concepts such as causal effect and confounding, b) identify the conditions required to estimate causal effects, and c) use analytical methods that, under those conditions, provide estimates that can be endowed with a causal interpretation. These (causal) methods can be used under less restrictive conditions than traditional statistical methods. For example, causal methods allow one to estimate the causal effect of a time-varying exposure in the presence of time-dependent confounders that lie on the causal pathway between exposure and outcome.
Teaching and learning activities Lectures and group sessions. The course is offered as full-time course over three days. Before the course, the student is required to study the course literature.
Compulsory elements The individual written examination (summative assessment).
Examination Individual written examination performed as a take-home examination after the course. Students who do not obtain a passing grade in the first examination will be offered a second examination within two months of the final day of the course.
Literature and other teaching material Recommended reading:
Hernán MA, Robins JM (2016). Causal Inference. Boca Raton: Chapman & Hall/CRC, forthcoming. The book can be downloaded (for free) from
Number of students 8 - 25
Selection of students Eligible doctoral students, with required prerequisite knowledge, prioritized according to 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) date for registration as a doctoral student (priority given to earlier registration date). To be considered, submit a completed application form. Give all information requested, including a description of current research and motivation for attending, and an account of previous courses taken.
More information The course focuses on a common framework for the analysis of randomized trials and observational studies for comparative effectiveness and safety research. In addition to exploring key challenges for comparative effectiveness research, the course critically reviews methods proposed to overcome those challenges. Methods in the context of several case studies for cancer, cardiovascular, renal, and infectious diseases are presented.
Additional course leader Course leader is Miguel A. Hernán, Professor of Epidemiology, Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA, website:
Latest course evaluation Not available
Course responsible Anita Berglund
The institute of Environmental Medicine
Contact person Johanna Bergman
Institutet för miljömedicin

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